Face recognition has been widely employed in a variety of applications. Like any other biometric modality, a critical concern in face recognition is to detect spoofing attack. In the past decade, photos and videos are two popular media of carrying out spoofing attacks and varieties of face anti-spoofing algorithms have been proposed and encouraging results have been obtained. Recently, with the rapid development of 3D reconstruction and material technologies, 3D mask attack becomes a new challenge to face recognition since affordable off-the-shelf masks have been shown to be able to spoof existing face recognition system. Unlike the success in traditional photo or video based face anti-spoofing, very few methods have been proposed to address 3D mask face anti-spoofing. To the best of the inventors' knowledge, most existing face anti-spoofing methods are not able to tackle this new attack, since 3D masks have similar appearance and geometry properties as live faces.
As known in the general common knowledge, texture-based methods are the few effective approaches that have been evaluated on 3D mask attack problem. Experimental results demonstrate their discriminative ability on 3D mask attack database and morpho datasets with different classifiers. Through concatenation of different local binary pattern settings, multi-scale local binary pattern can effectively capture the subtle texture differences between genuine and masked faces and achieves 99.4% area under the curve on 3D mask attack database dataset. Although the results are promising, the problem of the cross-dataset (where training and testing data are selected from different datasets) scenario remains open. From the application perspective, it is essential for a face anti-spoofing method to be effective and robust to different mask types and video qualities. In fact, as known in the general common knowledge, the present problem cannot be well generalized under inter-test (cross-dataset) protocol. This is because of the over-fitting problem of its intrinsic data-driven nature. Also, since texture-based methods rely on the appearance differences between 3D masks and genuine faces, it may not work for the super realistic masks that have imperceptible difference with the genuine face, e.g., prosthetics makeup. Therefore, there is a need to provide a more effective face anti-spoofing method to identify super realistic masks or partially masked faces.